1. Field of the Invention
The present invention relates to a pathological image diagnosis support system for supporting clinical diagnosis by computer that processes an image converted into digitized data.
2. Description of the Related Art
In the pathological diagnosis of cancer, a “tissue examination” or histopathology in which a pathologist observes a specimen of a focal tissue, which has been collected by a needle biopsy or a surgical operation, with a microscope to perform the diagnosis of its benignancy and malignancy, and a “cytological examination” or cytopathology in which a pathologist also observes a cell specimen in secreta such as sputum with a microscope to determine its benignancy or malignancy, are widely practiced.
A pathological image diagnosis support system is an apparatus for supporting such diagnosis by computer processing of digitized images. Patent Document 1 (Japanese Patent Laid-Open No. S57-153367) discloses a method in which the size of the nucleus of an individual cell, the staining intensity within the nucleus, and the like in an image are measured and the degree of the malignancy of the cell is determined based on the measured values.
Further, Patent Document 2 (Japanese Patent Laid-Open No. S62-135767) discloses a method in which feature parameters of a cell image are extracted, and normal and abnormal cells are distinguished in two steps. Further, Patent Document 3 (Japanese Patent Laid-Open No. S58-223868) discloses a method in which the cytoplasm and nucleus of a cell are detected concurrently using an isolated cell processing part and a clustered cell processing part to diagnose the benignancy and malignancy of the cell.
These inventions are based on a methodology to examine individual cells to determine the benignancy or malignancy thereof, and therefore it may be considered that they are primarily intended for cytological examination. This is because, a cytological examination by definition does not allow observing the macroscopic structure of tissue and therefore a cytological examination is an examination to investigate individual cells to determine their benignancy or malignancy.
On the other hand, in the tissue examination, the pathologist observes macroscopic structural features of tissue besides the information concerning individual cells and combines those to determine benignancy or malignancy. Because of this, it is said that the tissue examination enables more accurate determination.
Accordingly, Patent Document 4 (Japanese Patent Laid-Open No. H10-197522) discloses a method in which macroscopic information, such as the number of cell nuclei, is extracted to determine the conformity of a tissue image to a predetermined plurality of diagnostic categories which represent pathohistological features.
Further, Patent Document 5 (Japanese Patent Laid-Open No. 2001-59842) discloses a method in which the features based on the positional relationship or distribution pattern of cavities and cell nuclei are converted into numerical forms, and this information is used to determine of benignancy or malignancy.
Further, Patent Document 6 (Japanese Patent Laid-Open No. 2006-153742) discloses a pathological diagnosis support system and method in which a sub-image centered around the cell nucleus, hole, cytoplasm, stroma, or the like is extracted including periphery to determine the presence or absence of a tumor and the benignancy and malignancy of the tumor based on the sub-image.
FIG. 1 is a block diagram to show the configuration of the pathological diagnosis support system disclosed by Patent Document 6. As shown in FIG. 1, the support system includes learning pattern input means 100, learning pattern storage means 101, feature candidate generation means 102, feature determination means 103, feature storage means 104, classifying table generation means 105, and classifying table 106.
Learning pattern input means 100 extracts a sub-image centered around a cell nucleus, hole, cytoplasm, stroma, or the like from a pathological image to be diagnosed, and stores the sub-image in learning pattern storage means 101.
Learning pattern storage means 101 is means for storing and retaining a desired number of sub-images to be used for learning.
Feature candidate generation means 102 is means for successively generating feature candidates from a predetermined number of feature parameter sets.
Feature determination means 103 is means for determining a set of features most suitable for pattern recognition among the feature candidates generated by feature candidate generation means 102.
Feature storage means 104 is means for storing and retaining the set of features determined by feature determination means 103.
Classifying table generation means 105 is means for generating classifying table 106 for performing diagnosis using the set of features determined by feature determination means 103.
The invention according to Patent Document 6 takes into consideration that a cell nucleus and peripheral tissue thereof are stained to a respective inherent color since the tissue collected in a pathological examination is subject to staining (staining by hematoxylene or eosin etc.), and color information of the cell nucleus is extracted at the same time as extracting a sub-image centered around a cell nucleus, hole, cytoplasm, stroma, or the like from a pathological image to store both as feature candidates so that the presence or absence of a tumor, and the benignancy and malignancy of the tumor can be determined with a higher accuracy.
When intended for tissue examination, the above described invention does not offer high detection accuracy especially for poorly differentiated cancers in the adenocarcinoma. This is because that the analysis of the structure of a gland duct in a tissue image has not been conducted in the related art.
In the determination of an “adenocarcinoma” which is a cancer of a gland duct cell, a pathologist observes the gland duct structure as well, and this information is of great utility for determining benignancy and malignancy. Especially, in an adenocarcinoma which is called a “poorly differentiated cancer”, since cancer cells do not form a gland duct structure, the presence and absence of a normal gland duct structure provide an important measure for making the determination.
Thus, since there has not been any method of analyzing the gland duct structure and using that information for making a determination even though the information about the gland duct structure is an important measure for determining benignancy and malignancy, a problem has existed in that the determination accuracy has not reached a sufficient level. Adenocarcinomas are a critical cancer which widely takes place in digestive organs such as the stomach and large intestine etc. and genital organs such as the mammary gland etc.
Although Patent Document 4 or 5 describes that a cavity in a tissue image is analyzed and that information is used for the determination, the cavity analyzed in those inventions refers to a spacial gap peculiar to a cancer type called “cribriform cancer”, and is totally different from the cavity seen in a normal gland duct.
According to the invention described in Patent Document 6, although it is possible to determine the presence or absence of a tumor and the benignancy or malignancy of the tumor with high accuracy and in a short time based on a sub-image by extracting a sub-image centered around a cell nucleus, hole, cytoplasm, stroma, or the like from a pathological image and storing the sub-image as a learning pattern and input pattern, taking into consideration that changes in the cell nuclei and peripheral tissues thereof etc. are important factors to discriminate whether a tumor is benignant or malignant, there has always been a demand to improve the determination accuracy thereof.
3. Summary of the Invention
It is an objective of the present invention to provide a pathological image diagnosis support apparatus and a poorly differentiated cancer detection module for implementing the same, which is targeted for tissue examination and which has a high determination accuracy for poorly differentiated cancers in the adenocarcinoma.
The poorly differentiated cancer detection module of the present invention is a poorly differentiated cancer detection module for detecting a cancer cell from an inputted pathological tissue image, comprising:
cancer cell detection means for detecting a cancer cell from the pathological tissue image,
gland duct detection means for detecting a gland duct region from the pathological tissue image, and
search region limiting means for excluding the gland duct region detected by the gland duct detection means from a cancer cell search region in which the cancer cell detection means detects a cancer.
The poorly differentiated cancer detection module according to another embodiment of the present invention is a poorly differentiated cancer detection module for detecting a cancer cell from an inputted pathological tissue image, comprising:
cancer cell detection means for detecting a cancer cell from the pathological tissue image,
gland duct detection means for detecting a gland duct region from the pathological tissue image,
search region limiting means for excluding the gland duct region detected by the gland duct detection means from a cancer cell search region in which the cancer cell detection means detects a cancer,
gland duct density calculation means for calculating a gland duct density which is a density of the gland duct detected by the gland duct detection means in the vicinity of a plurality of detection points detected as a cancer cell by the cancer cell detection means in the pathological tissue image,
cancer cell density calculation means for calculating a cancer cell density which is the density of the cancer cell detected by the cancer cell detection means in the vicinity of the plurality of detection points, and
false detection rejection means for determining whether or not each detection point detected by the cancer cell detection means is a false detection based on the gland duct density and the cancer cell density calculated in the vicinity of the plurality of detection points, and rejecting the false detection.
The pathological image diagnosis support apparatus of the present invention comprises the above described poorly differentiated detection module.
The method of detecting a poorly differentiated cancer of the present invention is a method of detecting a poorly differentiated cancer for a cancer cell from an inputted pathological tissue image, wherein
cancer cell detection means detects a cancer cell from the pathological tissue image,
gland duct detection means detects a gland duct region from the pathological tissue image, and
search region limiting means excludes the gland duct region from a cancer cell search region in which the cancer cell detection means detects a cancer.
The method of detecting a poorly differentiated cancer according to another embodiment of the present invention is a method of detecting a poorly differentiated cancer for a cancer cell from an inputted pathological tissue image, wherein
cancer cell detection means detects a cancer cell from the pathological tissue image,
gland duct detection means detects a gland duct region from the pathological tissue image,
search region limiting means excludes the gland duct region from a cancer cell search region in which the cancer cell detection means detects a cancer,
gland duct density calculation means calculates gland duct density which is a density of the gland duct detected by the gland duct detection means in the vicinity of a plurality of detection points in the pathological tissue image, the detected points being detected as a cancer cell by the cancer cell detection means,
cancer cell density calculation means calculates a cancer cell density which is the density of the cancer cell detected by the cancer cell detection means in the vicinity of the plurality of detection points, and
false detection rejection means determines whether or not each detection point detected by the cancer cell detection means is a false detection based on the gland duct density and the cancer cell density calculated in the vicinity of the plurality of detection points to reject the false detection.
The program of the present invention causes a computer system to execute the above described method.
The recording medium of the present invention stores the above described program.